In certain industries, e.g., cosmetics or laundry detergents, it is necessary to mix two or more different liquid compositions together to form a finished liquid product. Such two or more liquid compositions to be mixed may contain very different ingredients and may be characterized by different colors, viscosities, and/or solubility. Depending on the mixing equipment and methods employed, it is possible that the mixing of such liquid compositions may not be carried out thoroughly. Correspondingly, the resulting finished liquid product may not be completely homogeneous, i.e., it may contain non-homogeneous spots or regions where the local characteristics are different from the rest of the product.
In order to understand the effectiveness of the mixing equipment and process, it is important to be able to objectively and quantitatively evaluate the in situ presence, severity and size of these non-homogeneous spots or regions in the finished liquid product. However, challenges lie in the fact that although some of these non-homogeneous spots or regions may be readily visible to human eyes, e.g., with different colors or solubility, others may be very subtle or even invisible, e.g., different only in ingredients or viscosities. Further, such non-homogenous spots or regions may not be discrete but rather gradual and subtle, which increases the difficulty in evaluating and quantifying them. Still further, when such non-homogenous spots or regions are formed when the finished liquid product has already been placed into its primary package, it is even more difficult to evaluate them through the primary package, e.g., a bottle (but taking the finished liquid product out of the bottle may disturb the non-homogeneity and defeat the purpose of in situ measurement).
Various image processing techniques have been previously employed to evaluate the mixing results of a garden variety of solid and/or liquid materials.
For example, Juez et al., “Monitoring of Concrete Mixing Evolution Using Image Analysis”, Powder Technology 305 (2017) 477-487 describes the use of an overall image histogram elaboration method to conduct inline monitoring of concrete mixing, especially the granulation and wet agglomeration of concrete. This method provides good results for images that are relatively uniform with little or no change in the overall luminosity. However, it does not work well when the luminosity and/or color variations are very high, as for some liquid mixtures formed by liquid compositions of different colors and/or solubility. The high luminosity and/or color variations generate “noises” that may mask non-homogeneous mixing results in some situations, but in other situations may incorrectly flag out homogenously mixed product. Even when a perfectly mixed product is provided, image of the bottle containing such product may not have a homogeneous histogram distribution and may result in an erroneously high non-homogeneity score.
For another example, Karami et al., “A Novel Image Analysis Approach for Evaluation of Mixing Uniformity in Drug-Filled Silicone Rubber Matrix”, International Journal of Pharmaceutics 460 (2014) 158-164 describes the use of image segmentation techniques for identifying and extracting discrete drug particles from digital images of drug formulations containing the same. The image segmentation techniques are more robust against environment fluctuations and background noises, in comparison with the overall image histogram elaboration technique mentioned hereinabove. However, such image segmentation method requires a sufficiently strong color or luminosity contrast between the particles to be identified and the background, and it therefore may not work well for identifying subtle and gradual changes in color and luminosity, which are typically seen in the non-homogenously mixed liquid products of interest to this invention.
There is therefore a need for an objective and quantitative image processing method for holistically evaluating and measuring the homogeneity and/or non-homogeneity of liquid products that are formed by mixing of two or more different liquid compositions, i.e., wellness of mixing.
It is desirable that such method is robust against overall color/luminosity fluctuations from image to image and background noises. It is also desirable that such method can effectively identify subtle irregularities and gradual changes in color and luminosity.
It is further advantageous if such method enables an unsupervised automated analysis that does not require a control or reference image.